Simulation of Wind Power Plants · (single turbine) Detailed turbine dynamics and control,...

DNV GL © 14 September 2017 SAFER, SMARTER, GREENERDNV GL ©

14 September 2017Ervin Bossanyi

Simulation of wind power plants

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4th Workshop on Systems Engineering for Wind EnergyRisø, Denmark

DNV GL © 14 September 2017

What?

Time domain simulation of a wind farm as a power station

Capability to represent:

– All external environmental conditions across the site: wind, wave, current, etc.

– Flow within the wind farm array accounting for atmospheric and wake effects

– Performance, loading and control of each individual turbine + support structure

– Electrical interactions between turbines and with the grid

– Control of the wind power station as a whole

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Why?

Improve wind farm design & layout– More detailed understanding of terrain, wake & electrical interaction effects

Development and testing of wind farm controls– Minimise wake interactions (induction control, wake steering, sector management)

– Provide grid ancillary services (curtailment, ramp rate limits, delta control, frequency response)

– Optimise supervisory control at wind farm level (high wind shutdown / rampdown, low voltage ride-through)

Optimisation of O&M strategies– Understanding conditions experienced by individual turbines

– Planning of scheduled maintenance

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How?

×Not one tool based on one simulation model, but:

Framework or toolbox within which different models can be linked together into a simulation platform tailored to any specific problem: A database structure containing all the fundamental parameters relevant to the wind power station A set of modelling tools of different levels of sophistication for addressing problems of different complexity A framework containing interfaces to the database and the modelling tools A user interface and workflow management system

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Physical components

A wind power station is a complex combination of coupled physical systems that dictate how the entire power station behaves:

– Topographical flow effects (onshore sites)

– Metocean conditions (offshore sites)

– Atmospheric stability and turbulence

– Dynamic wind turbines with individual controllers

– Turbine wake effects (affected by atmospheric turbulence)

– Wind farm electrical systems and interconnections

– Grid connection, and interaction with the external electrical network

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Constraints

Economic revenues:– From electricity production

– From providing grid ancillary services

Operational costs / loss of revenue– Operation and maintenance costs

– Environmental conditions imposed on operation (e.g. noise constraints)

– Curtailment demands imposed by network operators

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Examples of typical timescales

Timesteps:<1ms: electrical transients

~10ms: turbine control & loads, electromechanical interactions

~1 sec: turbine supervisory control and wake dynamics

1-10 min: farm level control

10-60 min: energy trading & forecasting

~1 day: O&M planning

∞ (steady-state): Farm layout design

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Length of simulations:~1s: electrical transients

~1 min: grid interaction: LVRT, frequency response

10-60 min: farm control for specific wind conditions, including ancillary services

1hr – 1 year: supervisory control

1 week – 25 years: O&M strategy


Wind field modelling

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Temporal variation:– Constant conditions, e.g. for simple simulations up to 10 minutes

– Low frequency variations, e.g. for some supervisory controls, energy trading, etc.

– Turbulence: 10-60 minute simulations with turbine & wake dynamics

– Turbulence + low frequencies: supervisory / farm control with turbine & wake dynamics

Spatial variation:– None

– Steady-state: Terrain / topographical effects

– Dynamic: Correlation of temporal variations across the wind farm


Pre-existing tools, further development and links

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Bladed (single turbine)

Detailed turbine dynamics and control, turbulent wind

Generates fatigue loads, power etc.

(0.01s for 10 minutes)

Dynamic wind farm simulator Turbine model as LongSim

Wind field correlated across wind farm (low frequency + turbulence)

Wakes with wake dynamics, meandering

Wind farm control

(1s for hours – weeks – years)

WindFarmer (wind farm)

Terrain & wakes

Energy calculation

Layout optimisation

(steady state)

CFD (wind farm)

Terrain & wakes

RANS

(steady state)

Set-point optimiser (wind farm)

Power (delta) set-point and yaw offsets

Pre-calculated fatigue look-up

(steady state)

LongSim (single turbine )

Full turbine control with simplified dynamics, pre-calculated fatigue look-up

Low-frequency wind plus turbulence

(1s for hours – weeks – years)

Cost model


Dynamic wind farm simulator: 9-turbine example

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One-hour simulation took 4 minutes on a lap-top (using one core)

• Contour plot of wind speed

• Turbines show yaw position and local wind vector

Look out for:• Turbulence advecting and evolving

• Wakes developing and meandering

• Wind direction changing (SSE to SSW)

• Turbine yaw control follows


Dynamic wind farm simulator: Horns Rev 1 (80 turbines)

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• Low wind speed

• Rapid direction change -~90º in a few minutes

• Direction change propagates through the farm at mean wind speed

• Faster than real time running on a single core


Toolbox vision – other interconnections

More direct integration– CFD for higher-fidelity terrain & wake flow calculations

– Site layout optimisation

– Turbine aeroelastic model (Bladed)

– Cost models

Other components– Electrical models such as DigSILENT, PSCAD etc.

– Grid operational models (e.g. KERMIT)

– O&M planning models

– Market models

– Forecasting

– Etc.

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Example application: control of wind farm wakes

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• Reduced power!• increased loading!

Switch this turbine off?

Or reduce the power set-point of this one?

Or maybe yaw the turbine slightly to steer its wake away from the

next turbine?

1. What is the optimum* distribution of power and yaw set-points for all the turbines, in this wind condition?

2. How can we maintain optimum* performance in dynamically changing circumstances?

* Optimum has to be defined –depends on energy and loading


Control of wind farm wakes: Process

Bladed: pre-calculate performance and fatigue loads

Cost model: Define cost function for optimisation

Steady-state optimiser: calculate set-points

Dynamic wind farm simulator: simulate performance with realistically changing dynamic conditions

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0

50

100

150

200

250

300

350

400

1 2 3 4 5 6

Del

ta (

kW)

Turbine number

9 m/s 85º

10 m/s 85º

11 m/s 85º

9 m/s 90º

10 m/s 90º

11 m/s 90º

9 m/s 95º

10 m/s 95º

11 m/s 95º

Example resultsRow of 6 turbines, 3-hour simulation with changing wind conditions

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Fino1 mast 05-09-2003 13:20 to 16:30Wind Speed [m/s] Turbulence intensity [%]

Time [hour]

3

4

5

6

7

8

9

10

11

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Fino1 mast 05-09-2003 13:20 to 16:30Wind direction [deg]

Time [hour]

88

89

90

91

92

93

94

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

FINO-1 data:

Optimal set-point matrix calculated for turbulence levels 4%, 7%, 10%

0

50

100

150

200

250

300

350

400

1 2 3 4 5 6

Del

ta (

kW)

Turbine number

4% turbulence 7% turbulence

Wind speed

Turbulence intensity

Wind direction


Example results: simulation output

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Run 'finobase hifreq' Run 'finoopt hifreq'

MYT

Tot

al [

MNm

]

0.16

0.18

0.20

0.22

0.24

0.26

0.28

0.30

0.32Fatigue sum

Run finobase hifreq Run finoopt hifreq

Farm

Powe

r [M

W]

3.0

3.5

4.0

4.5

5.0

5.5

'finoopt hifreq']Turbine1Delta Turbine2Delta Turbine3Delta Turbine4Delta Turbine5Delta Turbine6D

[MW

]

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Power reduction set-pointsTotal power

Set-points change with wind conditions

Large reduction in fatigue loading

Slight increase in energy production


Conclusions

A basic (usable) toolset has been created, by extending several existing codes, and linking them (in ad-hoc fashion, so far…)

Many components already validated up to a point, but more detailed validation is needed (interaction of sub-models, wider range of conditions, etc.). This will undoubtedly lead to improved or better-calibrated sub-models

Improved or alternative sub-models can be easily plugged in as they become available

Integration framework does not yet exist formally, and some components have not yet been linked at all (e.g. grid model)

Next steps Validation → improvement of sub-models

Further component integration and software structure design

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SAFER, SMARTER, GREENER

www.dnvgl.com

Thank you

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[email protected]

Simulation of Wind Power Plants · (single turbine) Detailed turbine dynamics and control,...

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